Triple
T35885802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Henderson vs Michael Bisping |
E1037638
|
entity |
| Predicate | HendersonNickname |
P185367
|
FINISHED |
| Object | Hendo |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Hendo | Statement: [Dan Henderson vs Michael Bisping, HendersonNickname, Hendo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: HendersonNickname Context triple: [Dan Henderson vs Michael Bisping, HendersonNickname, Hendo]
-
A.
houstonNickname
Indicates that one entity is a nickname or informal moniker used to refer to Houston.
-
B.
regionalNickname
Indicates that one entity is an informal or colloquial name used for another entity within a specific geographic region.
-
C.
nicknameOfTown
Indicates that one entity is a nickname or informal name used to refer to a particular town.
-
D.
nicknamedFor
Indicates that one entity serves as the source, inspiration, or reason for another entity’s nickname.
-
E.
cityNickname
Indicates that one entity is commonly used as an informal or alternative name for a city.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e1f4d748190bb55594d8441d70e |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be520f148190ba200bf3dbf40656 |
completed | May 3, 2026, 9:29 p.m. |
Created at: May 3, 2026, 4:06 p.m.